A new bound in information theory

Author

Popescu, Mihaela-Alexandra ; Slusanschi, Emil ; Iancu, Voichita ; Pop, Florin

Author_Institution

Inst. of Solid Mech., Bucharest, Romania

fYear

2014

fDate

11-13 Sept. 2014

Firstpage

1

Lastpage

3

Abstract

Shannon Entropy, for discrete-valued random variables, plays important roles in information theory [1], especially for the transmission, processing and storage of information and also in measure theory with major impact to data integration and probabilistic estimation. The purpose of this paper is to present a new bound for the Shannon Entropy, by first developing a refinement of Jensen´s inequality. This refinement is applied in order to find a new and more accurate upper bound for Shannon Entropy. Based on this, the paper presents an application to structural complexity analysis of a computer network modeled by a connected graph.

Keywords

computational complexity; computer networks; entropy; graph theory; Jensen inequality; Shannon entropy; computer network; connected graph; data integration; discrete-valued random variables; information processing; information storage; information theory; information transmission; probabilistic estimation; structural complexity analysis; Analytical models; Complexity theory; Computational modeling; Computer networks; Entropy; Information theory; Upper bound; Jensen´s inequality; bounds; entropy; refinements;

fLanguage

English

Publisher

ieee

Conference_Titel

RoEduNet Conference 13th Edition: Networking in Education and Research Joint Event RENAM 8th Conference, 2014

Conference_Location

Chisinau

ISSN

2068-1038

Print_ISBN

978-1-4799-6860-2

Type

conf

DOI

10.1109/RoEduNet-RENAM.2014.6955301

Filename

6955301